Antigravity Agent
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10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
223 lines
7.2 KiB
Markdown
223 lines
7.2 KiB
Markdown
---
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id: wiki-2026-0508-오픈소스-이미지-모델-미세-조정-및-배포
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title: 오픈소스 이미지 모델 미세 조정 및 배포
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Open-Source Image Model Fine-tuning, OSS Image Model Deploy, FLUX/SDXL Fine-tune]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [ai, fine-tuning, lora, flux, sdxl, deployment]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: diffusers-peft-vllm
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---
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# 오픈소스 이미지 모델 미세 조정 및 배포
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## 매 한 줄
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> **"매 LoRA 30분 + ComfyUI/vLLM 배포 = production image gen"**. 2026 의 OSS image stack (FLUX.1, SDXL, SD3.5) 은 LoRA/DoRA 기반 PEFT, FP8/INT4 양자화, ComfyUI/Automatic1111/Replicate 배포를 통해 single GPU 에서 가동 가능하다. 매 closed API 와 비교해 cost 1/10, full control 의 장점이 압도적.
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## 매 핵심
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### 매 OSS model 선택 (2026)
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- **FLUX.1-dev**: 12B params, photorealism + prompt fidelity 최강.
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- **FLUX.1-schnell**: 4-step 빠른 inference, Apache 2.0.
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- **SDXL 1.0 + Turbo**: ecosystem 풍부, LoRA 호환성 최고.
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- **SD 3.5 Large**: MMDiT, 8B params.
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- **AuraFlow / PixArt-Sigma**: 경량 alternative.
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### 매 fine-tune 방법
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- **LoRA**: rank 16-64, target attention.
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- **DoRA**: weight-decomposed LoRA, 더 안정적.
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- **Full fine-tune**: rare, 80GB+ VRAM.
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- **Textual Inversion**: token embedding only.
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- **Dreambooth**: subject-driven, 매 LoRA 와 결합.
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### 매 응용
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1. Brand asset gen 의 in-house pipeline.
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2. Character consistency 의 LoRA library.
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3. On-device gen 의 quantized deploy.
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## 💻 패턴
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### LoRA 학습 (FLUX, ai-toolkit)
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```yaml
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# config/flux_lora.yaml
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job: extension
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config:
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name: hero_character_v1
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process:
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- type: sd_trainer
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training_folder: ./output
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device: cuda:0
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network:
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type: lora
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linear: 32 # rank
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linear_alpha: 32
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train:
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batch_size: 1
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steps: 2000
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gradient_accumulation_steps: 4
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train_unet: true
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train_text_encoder: false
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lr: 1e-4
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optimizer: adamw8bit
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datasets:
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- folder_path: ./data/hero
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caption_ext: txt
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resolution: [512, 768, 1024]
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model:
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name_or_path: black-forest-labs/FLUX.1-dev
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is_flux: true
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quantize: true # 8-bit base for fit
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```
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### 학습 실행
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```bash
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git clone https://github.com/ostris/ai-toolkit && cd ai-toolkit
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pip install -r requirements.txt
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python run.py config/flux_lora.yaml
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# 30 min on RTX 4090, output: hero_character_v1.safetensors
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```
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### Quantization (FP8 / INT4)
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```python
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from optimum.quanto import quantize, qfloat8, freeze
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from diffusers import FluxPipeline
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import torch
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pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev",
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torch_dtype=torch.bfloat16)
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quantize(pipe.transformer, weights=qfloat8)
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freeze(pipe.transformer)
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quantize(pipe.text_encoder_2, weights=qfloat8)
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freeze(pipe.text_encoder_2)
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pipe.to("cuda")
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# VRAM: 24GB → 14GB
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```
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### Inference server (FastAPI + diffusers)
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```python
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from fastapi import FastAPI
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from pydantic import BaseModel
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from diffusers import FluxPipeline
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import torch, io, base64
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app = FastAPI()
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pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev",
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torch_dtype=torch.bfloat16).to("cuda")
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pipe.load_lora_weights("./loras/hero.safetensors")
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class Req(BaseModel):
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prompt: str
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steps: int = 28
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guidance: float = 3.5
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seed: int | None = None
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@app.post("/generate")
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def generate(r: Req):
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gen = torch.Generator("cuda").manual_seed(r.seed) if r.seed else None
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img = pipe(r.prompt, num_inference_steps=r.steps,
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guidance_scale=r.guidance, generator=gen).images[0]
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buf = io.BytesIO(); img.save(buf, format="PNG")
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return {"image_b64": base64.b64encode(buf.getvalue()).decode()}
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```
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### ComfyUI workflow (JSON node graph)
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```json
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{
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"1": {"class_type":"CheckpointLoaderSimple",
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"inputs":{"ckpt_name":"flux1-dev.safetensors"}},
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"2": {"class_type":"LoraLoader",
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"inputs":{"model":["1",0],"clip":["1",1],
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"lora_name":"hero.safetensors",
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"strength_model":0.9,"strength_clip":0.9}},
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"3": {"class_type":"CLIPTextEncode",
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"inputs":{"clip":["2",1],"text":"<hero> in a forest, cinematic"}},
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"4": {"class_type":"KSampler",
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"inputs":{"model":["2",0],"steps":28,"cfg":3.5,
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"sampler_name":"euler","scheduler":"simple"}}
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}
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```
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### Replicate / Modal deploy
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```python
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# Modal
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import modal
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app = modal.App("flux-lora")
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image = modal.Image.debian_slim().pip_install("diffusers","torch","accelerate","peft")
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@app.cls(gpu="A100-40GB", image=image)
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class Generator:
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@modal.enter()
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def load(self):
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from diffusers import FluxPipeline
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self.pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev").to("cuda")
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self.pipe.load_lora_weights("./hero.safetensors")
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@modal.method()
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def generate(self, prompt: str):
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return self.pipe(prompt).images[0]
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```
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### Caption automation (BLIP/Florence-2)
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```python
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from transformers import AutoProcessor, AutoModelForCausalLM
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proc = AutoProcessor.from_pretrained("microsoft/Florence-2-large")
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model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large").cuda()
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def caption(img):
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inputs = proc(text="<MORE_DETAILED_CAPTION>", images=img, return_tensors="pt").to("cuda")
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out = model.generate(**inputs, max_new_tokens=256, num_beams=3)
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return proc.batch_decode(out, skip_special_tokens=True)[0]
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# Batch caption training data
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for f in dataset_folder.glob("*.png"):
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Path(f.with_suffix(".txt")).write_text("<myStyle> " + caption(open_image(f)))
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```
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## 매 결정 기준
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| 상황 | Approach |
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| photoreal + prompt fidelity | FLUX.1-dev |
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| 빠른 prototyping | FLUX.1-schnell |
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| custom subject | LoRA + Dreambooth |
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| 24GB GPU 미만 | FP8 quantization |
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| no-code workflow | ComfyUI |
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| serverless scale | Modal / Replicate |
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| commercial use | Apache/SAI license 확인 |
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**기본값**: FLUX.1-dev + LoRA rank 32 + ComfyUI for prototyping, Modal for prod.
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## 🔗 Graph
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- 부모: [[AI 이미지 생성 (AI Image Generation)]]
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- 변형: [[LoRA Fine-tuning]]
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- 응용: [[인공지능 시각 언어 생성 (AI Visual Language Generation)]] · [[일관된 캐릭터 및 스타일 구축]]
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- Adjacent: [[LLM_Optimization_and_Deployment_Strategies|Quantization]] · [[ComfyUI]]
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## 🤖 LLM 활용
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**언제**: training caption authoring, hyperparameter sweep planning, pipeline debugging.
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**언제 X**: visual aesthetic judgment — human eval 필요.
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## ❌ 안티패턴
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- **No caption strategy**: same caption 매 image — model 이 trigger token ignore.
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- **Rank too high**: rank 256 → overfit + huge file.
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- **Skipping validation set**: train loss only, no FID/CLIP score.
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- **License blindness**: commercial use restriction 의 무시.
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## 🧪 검증 / 중복
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- Verified (BFL FLUX docs 2025, ai-toolkit repo, diffusers 0.32+, Modal docs).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — OSS image model fine-tune + deploy stack. |
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